AI Applications in Digital Advertising

​AI (artificial intelligence) is reshaping advertising. By using AI, advertisers can better target potential customers and optimize advertising campaigns. With real-time campaign tracking and reporting, advertisers can optimize a campaign on the go, while it counts.

AI encompasses several other smart technologies, like machine learning (ML), natural language processing (NLP), natural language generation (NLG), neural networks, deep learning, and speech recognition. These technologies can learn and improve independently, offering advertisers several use cases.

Then there's programmatic media buying - automatic buying of digital ad space. Before AI, the best media buyers could do was buy ad spaces where their target audiences congregated - magazines, TV, and other media. Today, they can do much more. Programmatic media buying allows advertisers to push targeted advertising, ensuring their ads reach their most relevant customers, thanks to ML.

A good example of programmatic advertising is Met Ads relevance score and ad frequency. An algorithm decides the optimal frequency with which a potential customer should see an ad. This eliminates unnecessary ad spending due to diminishing returns occasioned by overexposure to an ad.

Pre-AI, advertisers would wait until the end of an ad campaign to know what’s working and what’s not. AI provides real-time insights that advertisers can leverage to optimize not just their ad performance, but also their ad budget. For example, an advertiser running two different types of ads can let AI redirect ad funds from the weaker performing ad to the better performing one.

AI can also create ads. With NLP and NLG capabilities, algorithms can partially or fully create effective ads. AI can scour the digital space and uncover patterns relating to what kinds of ads are found in specific niches. Tools like ChatGPT can write ad copy and source other creative elements - in seconds and at a humanly impossible scale.

Additionally, AI can generate ad variations automatically. An advertiser can reproduce several variations of their best-performing ad. AI-generated variations, unlike human ones, are based on insights into what works. Variations could include different messaging angles and different ad formats and sizes.

AI can also tailor ad messaging to consumer motivations. Audiences, even those in the same demographic, have various motivations. Traditional advertisers had to choose which motivating factors to appeal to. AI can personalize a single ad to reflect different motivations by adjusting the language of the ad content to suit the viewer's preferences based on their online habits.

Before AI, advertisers couldn’t tell whether an ad campaign would be a winner or a loser. They relied on advertising best practices to decide which elements would stick. AI can predict how an ad will perform even before it launches. It does this by analyzing ads that have succeeded or failed and then applying the insights to new ads.

Not all potential customers are the same. Some buy after seeing an ad once, while others may need to be nurtured. Traditional advertisers had no way of measuring lead quality, which resulted in wasting resources and time. With AI-powered lead scoring, leads generated by ads are categorized based on their readiness and or willingness to take action. This can save time, especially in B2B (business to business) with its long sales cycle.

The power of AI lies in its unmatched computational power. It can crunch vast amounts of data and uncover patterns at a scale and speed that humans simply can't. It can also complete several advertising tasks simultaneously without any human involvement, from testing ads to analyzing and optimizing performance.

Chris Carlson

Shop